Distortion Measurement based Dynamic Packet Scheduling of Video Stream over IEEE 802.11e WLANs

( Wu Minghu ),( Chen Rui ),( Zhou Shangli ),( Zhu Xiuchang ) 한국인터넷정보학회 2013 KSII Transactions on Internet and Information Syst Vol.7 No.11

In H.264, three different data partition types are used, which have unequal importance to the reconstructed video quality. To improve the performance of H.264 video streaming transmission over IEEE 802.11e Wireless Local Area Networks, a prioritization mechanism that categorizes different partition types to different priority classes according to the calculated distortion within one Group of Pictures. In the proposed scheme, video streams have been encoded based on the H.264 codec with its data partition enabled. The dynamic scheduling scheme based on Enhanced Distributed Channel Access has been configured to differentiate the data partitions according to their distortion impact and the queue utilization ratio. Simulation results show that the proposed scheme improves the received video quality by 1dB in PSNR compared with the existing Enhanced Distributed Channel Access static mapping scheme.

Distributed Video Compressive Sensing Reconstruction by Adaptive PCA Sparse Basis and Nonlocal Similarity

( Wu Minghu ),( Zhu Xiuchang ) 한국인터넷정보학회 2014 KSII Transactions on Internet and Information Syst Vol.8 No.8

To improve the rate-distortion performance of distributed video compressive sensing (DVCS), the adaptive sparse basis and nonlocal similarity of video are proposed to jointly reconstruct the video signal in this paper. Due to the lack of motion information between frames and the appearance of some noises in the reference frames, the sparse dictionary, which is constructed using the examples directly extracted from the reference frames, has already not better obtained the sparse representation of the interpolated block. This paper proposes a method to construct the sparse dictionary. Firstly, the example-based data matrix is constructed by using the motion information between frames, and then the principle components analysis (PCA) is used to compute some significant principle components of data matrix. Finally, the sparse dictionary is constructed by these significant principle components. The merit of the proposed sparse dictionary is that it can not only adaptively change in terms of the spatial-temporal characteristics, but also has ability to suppress noises. Besides, considering that the sparse priors cannot preserve the edges and textures of video frames well, the nonlocal similarity regularization term has also been introduced into reconstruction model. Experimental results show that the proposed algorithm can improve the objective and subjective quality of video frame, and achieve the better rate-distortion performance of DVCS system at the cost of a certain computational complexity.

Priority-based Unequal Error Protection Scheme of Data partitioned H.264 video with Hierarchical QAM

( Chen Rui ),( Wu Minghu ),( Yang Jie ),( Rui Xiongli ) 한국인터넷정보학회 2014 KSII Transactions on Internet and Information Syst Vol.8 No.11

In this paper, we propose a priority-based unequal error protection scheme of data partitioned H.264/AVC video with hierarchical quadrature amplitude modulation. In order to map data with higher priority onto the most significant bits of QAM constellation points, a priority sorting method categorizes different data partitions according to the unequal importance factor of encoded video data in one group of pictures by evaluated the average distortion. Then we propose a hierarchical quadrature amplitude modulation arrangement with adaptive constellation distances, which takes into account the unequal importance of encoded video data and the channel status. Simulation results show that the proposed scheme improves the received video quality by about 2 dB in PSNR comparing with the state-of-the-art unequal error protection scheme, and outperforms EEP scheme by up to 5 dB when the average channel SNR is low.

Joint Overlapped Block Motion Compensation Using Eight-Neighbor Block Motion Vectors for Frame Rate Up-Conversion

( Ran Li ),( Minghu Wu ),( Zongliang Gan ),( Ziguan Cui ),( Xiuchang Zhu ) 한국인터넷정보학회 2013 KSII Transactions on Internet and Information Syst Vol.7 No.10

The traditional block-based motion compensation methods in frame rate up-conversion (FRUC) only use a single uniquely motion vector field. However, there will always be some mistakes in the motion vector field whether the advanced motion estimation (ME) and motion vector analysis (MA) algorithms are performed or not. Once the motion vector field has many mistakes, the quality of the interpolated frame is severely affected. In order to solve the problem, this paper proposes a novel joint overlapped block motion compensation method (8J-OBMC) which adopts motion vectors of the interpolated block and its 8-neighbor blocks to jointly interpolate the target block. Since the smoothness of motion filed makes the motion vectors of 8-neighbor blocks around the interpolated block quite close to the true motion vector of the interpolated block, the proposed compensation algorithm has the better fault-tolerant capability than traditional ones. Besides, the annoying blocking artifacts can also be effectively suppressed by using overlapped blocks. Experimental results show that the proposed method is not only robust to motion vectors estimated wrongly, but also can to reduce blocking artifacts in comparison with existing popular compensation methods.

Lightweight multiple scale-patch dehazing network for real-world hazy image

( Juan Wang ),( Chang Ding ),( Minghu Wu ),( Yuanyuan Liu ),( Guanhai Chen ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.12

Image dehazing is an ill-posed problem which is far from being solved. Traditional image dehazing methods often yield mediocre effects and possess substandard processing speed, while modern deep learning methods perform best only in certain datasets. The haze removal effect when processed by said methods is unsatisfactory, meaning the generalization performance fails to meet the requirements. Concurrently, due to the limited processing speed, most dehazing algorithms cannot be employed in the industry. To alleviate said problems, a light-weight fast dehazing network based on a multiple scale-patch framework (MSP) is proposed in the present paper. Firstly, the multi-scale structure is employed as the backbone network and the multi-patch structure as the supplementary network. Dehazing through a single net-work causes problems, such as loss of object details and color in some image areas, the multi-patch structure was employed for MSP as an information supplement. In the algorithm image processing module, the image is segmented up and down for processed separately. Secondly, MSP generates a clear dehazing effect and significant robustness when targeting real-world homogeneous and nonhomogeneous hazy maps and different datasets. Compared with existing dehazing methods, MSP demonstrated a fast inference speed and the feasibility of real-time processing. The overall size and model parameters of the entire dehazing model are 20.75M and 6.8M, and the processing time for the single image is 0.026s. Experiments on NTIRE 2018 and NTIRE 2020 demonstrate that MSP can achieve superior performance among the state-of-the-art methods, such as PSNR, SSIM, LPIPS, and individual subjective evaluation.

Infrared and visible image fusion based on Laplacian pyramid and generative adversarial network

( Juan Wang ),( Cong Ke ),( Minghu Wu ),( Min Liu ),( Chunyan Zeng ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.5

An image with infrared features and visible details is obtained by processing infrared and visible images. In this paper, a fusion method based on Laplacian pyramid and generative adversarial network is proposed to obtain high quality fusion images, termed as Laplacian-GAN. Firstly, the base and detail layers are obtained by decomposing the source images. Secondly, we utilize the Laplacian pyramid-based method to fuse these base layers to obtain more information of the base layer. Thirdly, the detail part is fused by a generative adversarial network. In addition, generative adversarial network avoids the manual design complicated fusion rules. Finally, the fused base layer and fused detail layer are reconstructed to obtain the fused image. Experimental results demonstrate that the proposed method can obtain state-of-the-art fusion performance in both visual quality and objective assessment. In terms of visual observation, the fusion image obtained by Laplacian-GAN algorithm in this paper is clearer in detail. At the same time, in the six metrics of MI, AG, EI, MS_SSIM, Q_abf and SCD, the algorithm presented in this paper has improved by 0.62%, 7.10%, 14.53%, 12.18%, 34.33% and 12.23%, respectively, compared with the best of the other three algorithms. abfQ

Instance segmentation with pyramid integrated context for aerial objects

Juan Wang,Liquan Guo,Minghu Wu,Guanhai Chen,Zishan Liu,Yonggang Ye,Zetao Zhang 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.3

Aerial objects are more challenging to segment than normal objects, which are usually smaller and have less textural detail. In the process of segmentation, target objects are easily omitted and misdetected, which is problematic. To alleviate these issues, we propose local aggregation feature pyramid networks (LAFPNs) and pyramid integrated context modules (PICMs) for aerial object segmentation. First, using an LAFPN, while strengthening the deep features, the extent to which low-level features interfere with high-level features is reduced, and numerous dense and small aerial targets are prevented from being mistakenly detected as a whole. Second, the PICM uses global information to guide local features, which enhances the network's comprehensive understanding of an entire image and reduces the missed detection of small aerial objects due to insufficient texture information. We evaluate our network with the MS COCO dataset using three categories: airplanes, birds, and kites. Compared with Mask R-CNN, our network achieves performance improvements of 1.7%, 4.9%, and 7.7% in terms of the AP metrics for the three categories. Without pretraining or any postprocessing, the segmentation performance of our network for aerial objects is superior to that of several recent methods based on classic algorithms.

Research on a handwritten character recognition algorithm based on an extended nonlinear kernel residual network

( Zheheng Rao ),( Chunyan Zeng ),( Minghu Wu ),( Zhifeng Wang ),( Nan Zhao ),( Min Liu ),( Xiangkui Wan ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.1

Although the accuracy of handwritten character recognition based on deep networks has been shown to be superior to that of the traditional method, the use of an overly deep network significantly increases time consumption during parameter training. For this reason, this paper took the training time and recognition accuracy into consideration and proposed a novel handwritten character recognition algorithm with newly designed network structure, which is based on an extended nonlinear kernel residual network. This network is a non-extremely deep network, and its main design is as follows:(1) Design of an unsupervised apriori algorithm for intra-class clustering, making the subsequent network training more pertinent; (2) presentation of an intermediate convolution model with a pre-processed width level of 2;(3) presentation of a composite residual structure that designs a multi-level quick link; and (4) addition of a Dropout layer after the parameter optimization. The algorithm shows superior results on MNIST and SVHN dataset, which are two character benchmark recognition datasets, and achieves better recognition accuracy and higher recognition efficiency than other deep structures with the same number of layers.

Side Information Extrapolation Using Motion-aligned Auto Regressive Model for Compressed Sensing based Wyner-Ziv Codec

( Ran Li ),( Zongliang Gan ),( Ziguan Cui ),( Minghu Wu ),( Xiuchang Zhu ) 한국인터넷정보학회 2013 KSII Transactions on Internet and Information Syst Vol.7 No.2

In this paper, we propose a compressed sensing (CS) based Wyner-Ziv (WZ) codec using motion-aligned auto regressive model (MAAR) based side information (SI) extrapolation to improve the compression performance of low-delay distributed video coding (DVC). In the CS based WZ codec, the WZ frame is divided into small blocks and CS measurements of each block are acquired at the encoder, and a specific CS reconstruction algorithm is proposed to correct errors in the SI using CS measurements at the decoder. In order to generate high quality SI, a MAAR model is introduced to improve the inaccurate motion field in auto regressive (AR) model, and the Tikhonov regularization on MAAR coefficients and overlapped block based interpolation are performed to reduce block effects and errors from over-fitting. Simulation experiments show that our proposed CS based WZ codec associated with MAAR based SI generation achieves better results compared to other SI extrapolation methods.